Keras Multi-layer Perceptron
Tensorflow Premade Estimators
Tensorflow Image and Text Classification
These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.
import numpy as np
import pandas as pd
from sklearn import datasets
data = datasets.load_wine()
Data = pd.DataFrame(data['data'],
columns = [x.title().replace('_',' ').replace('Of','of').replace('Od','OD') for x in data['feature_names']])
Labels_dict = dict(zip(list(np.sort(np.unique(data['target'].tolist()))),
list([x.title() for x in data['target_names']])))
Target = 'Class'
Data[Target] = data['target']
display(Data)
print(data['DESCR'])
| Alcohol | Malic Acid | Ash | Alcalinity of Ash | Magnesium | Total Phenols | Flavanoids | Nonflavanoid Phenols | Proanthocyanins | Color Intensity | Hue | OD280/OD315 of Diluted Wines | Proline | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 14.23 | 1.71 | 2.43 | 15.6 | 127.0 | 2.80 | 3.06 | 0.28 | 2.29 | 5.64 | 1.04 | 3.92 | 1065.0 | 0 |
| 1 | 13.20 | 1.78 | 2.14 | 11.2 | 100.0 | 2.65 | 2.76 | 0.26 | 1.28 | 4.38 | 1.05 | 3.40 | 1050.0 | 0 |
| 2 | 13.16 | 2.36 | 2.67 | 18.6 | 101.0 | 2.80 | 3.24 | 0.30 | 2.81 | 5.68 | 1.03 | 3.17 | 1185.0 | 0 |
| 3 | 14.37 | 1.95 | 2.50 | 16.8 | 113.0 | 3.85 | 3.49 | 0.24 | 2.18 | 7.80 | 0.86 | 3.45 | 1480.0 | 0 |
| 4 | 13.24 | 2.59 | 2.87 | 21.0 | 118.0 | 2.80 | 2.69 | 0.39 | 1.82 | 4.32 | 1.04 | 2.93 | 735.0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 173 | 13.71 | 5.65 | 2.45 | 20.5 | 95.0 | 1.68 | 0.61 | 0.52 | 1.06 | 7.70 | 0.64 | 1.74 | 740.0 | 2 |
| 174 | 13.40 | 3.91 | 2.48 | 23.0 | 102.0 | 1.80 | 0.75 | 0.43 | 1.41 | 7.30 | 0.70 | 1.56 | 750.0 | 2 |
| 175 | 13.27 | 4.28 | 2.26 | 20.0 | 120.0 | 1.59 | 0.69 | 0.43 | 1.35 | 10.20 | 0.59 | 1.56 | 835.0 | 2 |
| 176 | 13.17 | 2.59 | 2.37 | 20.0 | 120.0 | 1.65 | 0.68 | 0.53 | 1.46 | 9.30 | 0.60 | 1.62 | 840.0 | 2 |
| 177 | 14.13 | 4.10 | 2.74 | 24.5 | 96.0 | 2.05 | 0.76 | 0.56 | 1.35 | 9.20 | 0.61 | 1.60 | 560.0 | 2 |
178 rows × 14 columns
.. _wine_dataset:
Wine recognition dataset
------------------------
**Data Set Characteristics:**
:Number of Instances: 178 (50 in each of three classes)
:Number of Attributes: 13 numeric, predictive attributes and the class
:Attribute Information:
- Alcohol
- Malic acid
- Ash
- Alcalinity of ash
- Magnesium
- Total phenols
- Flavanoids
- Nonflavanoid phenols
- Proanthocyanins
- Color intensity
- Hue
- OD280/OD315 of diluted wines
- Proline
- class:
- class_0
- class_1
- class_2
:Summary Statistics:
============================= ==== ===== ======= =====
Min Max Mean SD
============================= ==== ===== ======= =====
Alcohol: 11.0 14.8 13.0 0.8
Malic Acid: 0.74 5.80 2.34 1.12
Ash: 1.36 3.23 2.36 0.27
Alcalinity of Ash: 10.6 30.0 19.5 3.3
Magnesium: 70.0 162.0 99.7 14.3
Total Phenols: 0.98 3.88 2.29 0.63
Flavanoids: 0.34 5.08 2.03 1.00
Nonflavanoid Phenols: 0.13 0.66 0.36 0.12
Proanthocyanins: 0.41 3.58 1.59 0.57
Colour Intensity: 1.3 13.0 5.1 2.3
Hue: 0.48 1.71 0.96 0.23
OD280/OD315 of diluted wines: 1.27 4.00 2.61 0.71
Proline: 278 1680 746 315
============================= ==== ===== ======= =====
:Missing Attribute Values: None
:Class Distribution: class_0 (59), class_1 (71), class_2 (48)
:Creator: R.A. Fisher
:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
:Date: July, 1988
This is a copy of UCI ML Wine recognition datasets.
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data
The data is the results of a chemical analysis of wines grown in the same
region in Italy by three different cultivators. There are thirteen different
measurements taken for different constituents found in the three types of
wine.
Original Owners:
Forina, M. et al, PARVUS -
An Extendible Package for Data Exploration, Classification and Correlation.
Institute of Pharmaceutical and Food Analysis and Technologies,
Via Brigata Salerno, 16147 Genoa, Italy.
Citation:
Lichman, M. (2013). UCI Machine Learning Repository
[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
School of Information and Computer Science.
.. topic:: References
(1) S. Aeberhard, D. Coomans and O. de Vel,
Comparison of Classifiers in High Dimensional Settings,
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Technometrics).
The data was used with many others for comparing various
classifiers. The classes are separable, though only RDA
has achieved 100% correct classification.
(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
(All results using the leave-one-out technique)
(2) S. Aeberhard, D. Coomans and O. de Vel,
"THE CLASSIFICATION PERFORMANCE OF RDA"
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
Mathematics and Statistics, James Cook University of North Queensland.
(Also submitted to Journal of Chemometrics).
Moreover, high variance for some features can hurt our modeling process. For this reason, we would like to standardize features by removing the mean and scaling to unit variance.
from sklearn.preprocessing import StandardScaler
X = Data.drop(columns = [Target])
y = Data[Target]
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
X_std = pd.DataFrame(data = X_std, columns = X.columns)
del scaler
PD = dict(figsize = (16, 5), hspace = 0.2, annot_text_size = 12, text_size = 14, word_break = 2)
from HD_DeepLearning import Feature_Normalize_Plot
Feature_Normalize_Plot(X, X_std, PD)
X_std.columns = [x.replace(' ','_').replace('/','_') for x in X_std.columns]
Feat_Dict = dict(zip(X.columns, X_std.columns))
import plotly.express as px
from HD_DeepLearning import DatasetTargetDist
Pull = [0 for x in range((len(Labels_dict)-1))]
Pull.append(.05)
PD = dict(PieColors = px.colors.sequential.BuPu[1:-1:3], TableColors = ['Navy','White'], hole = .4,
row_heights=[0.35, 0.65],textfont = 14, height = 520, tablecolumnwidth = [0.25, 0.15, 0.15],
pull = Pull, legend_title = Target, title_x = 0.5, title_y = 0.9, pie_legend = [0.1, 0.12])
del Pull
DatasetTargetDist(Data, Target, Labels_dict, PD)
StratifiedKFold is a variation of k-fold which returns stratified folds: each set contains approximately the same percentage of samples of each target class as the complete set.
from sklearn.model_selection import StratifiedShuffleSplit
def HD_StratifiedShuffleSplit(X, y, Test_Size = 0.3):
sss = StratifiedShuffleSplit(n_splits=1, test_size=Test_Size, random_state=42)
_ = sss.get_n_splits(X, y)
for train_index, test_index in sss.split(X, y):
# X
if isinstance(X, pd.DataFrame):
X_train, X_test = X.loc[train_index], X.loc[test_index]
else:
X_train, X_test = X[train_index], X[test_index]
# y
if isinstance(y, pd.Series):
y_train, y_test = y[train_index], y[test_index]
else:
y_train, y_test = y[train_index], y[test_index]
del sss
return X_train, y_train, X_test, y_test
X_train, y_train, X_test, y_test = HD_StratifiedShuffleSplit(X_std, y)
from HD_DeepLearning import Train_Test_Dist
PD.update(dict(column_widths=[0.3, 0.3, 0.3], tablecolumnwidth = [0.2, 0.4], height = 550, legend_title = Target))
Train_Test_Dist(X_train, y_train, X_test, y_test, PD, Labels_dict)
Here, we use the Tensorflow Linear classifier model.tf.estimator.DNNClassifier.
def input_fn(features, labels, training=True, batch_size=256):
"""An input function for training or evaluating"""
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle and repeat if you are in training mode.
if training:
dataset = dataset.shuffle(1000).repeat()
return dataset.batch(batch_size)
Moreover, an estimator model consists of two main parts, feature columns, and a numeric vector. Feature columns provide explanations for the input numeric vector. The following function separates categorical and numerical columns (features)and returns a descriptive list of feature columns.
import tensorflow as tf
def Feat_Columns(Inp):
Temp = Inp.dtypes.reset_index(drop = False)
Temp.columns = ['Features', 'Data Type']
Temp['Data Type'] = Temp['Data Type'].astype(str)
# Numeric_Columns
Numeric_Columns = Temp.loc[Temp['Data Type'].isin(['int64', 'int32', 'float64', 'float32']),'Features'].tolist()
# Categorical_Columns
Categorical_Columns = Temp.loc[Temp['Data Type'] == 'object','Features'].tolist()
# Feature Columns
feature_columns = []
if len(Categorical_Columns)>0:
for feature_name in Categorical_Columns:
vocabulary = Inp[feature_name].unique()
feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))
if len(Numeric_Columns)>0:
for feature_name in Numeric_Columns:
feature_columns.append(tf.feature_column.numeric_column(feature_name))
return feature_columns
my_feature_columns = Feat_Columns(X_std)
from IPython.display import clear_output
tf.keras.backend.clear_session()
IT = int(5e2)
classifier = tf.estimator.DNNClassifier(feature_columns = my_feature_columns,
# Hidden layers.
hidden_units = [1024, 512, 256],
# The model must choose between 3 classes.
n_classes = len(Labels_dict))
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps = IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))
| accuracy | average_loss | loss | global_step | |
|---|---|---|---|---|
| 0.9815 | 0.2498 | 0.2498 | 500 |
from HD_DeepLearning import ROC_Curve
# converting y_test to categorical
y_test_cat = tf.keras.utils.to_categorical(y_test, num_classes = len(Labels_dict), dtype='float32')
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
ROC_Curve(y_test_cat, probs, n_classes = len(Labels_dict), FS = 8)
The confusion matrix allows for visualization of the performance of an algorithm. Note that due to the size of data, here we don't provide a Cross-validation evaluation. In general, this type of evaluation is preferred.
from sklearn import metrics
from HD_DeepLearning import Confusion_Mat
# Train
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_train, y_train, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Train = pd.DataFrame(metrics.classification_report(y_train, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Train = metrics.confusion_matrix(y_train, y_pred)
# Test
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Test = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Test = metrics.confusion_matrix(y_test, y_pred)
Reports_Train = Reports_Train.reset_index().rename(columns ={'index': 'Train Set'})
Reports_Test = Reports_Test.reset_index().rename(columns ={'index': 'Test Set'})
display(Reports_Train.style.hide(axis='index').set_properties(**{'background-color': 'HoneyDew', 'color': 'Black'}).\
set_properties(subset=['Train Set'], **{'background-color': 'SeaGreen', 'color': 'White'}))
display(Reports_Test.style.hide(axis='index').set_properties(**{'background-color': 'Azure', 'color': 'Black'}).\
set_properties(subset=['Test Set'], **{'background-color': 'RoyalBlue', 'color': 'White'}))
PD = dict(FS = (10, 6), annot_kws = 14, shrink = .6, Labels = list(Labels_dict.values()))
Confusion_Mat(CM_Train, CM_Test, PD = PD, n_splits = None)
| Train Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Class_0 | 1.000000 | 1.000000 | 1.000000 | 41.000000 |
| Class_1 | 1.000000 | 0.980000 | 0.989899 | 50.000000 |
| Class_2 | 0.970588 | 1.000000 | 0.985075 | 33.000000 |
| accuracy | 0.991935 | 0.991935 | 0.991935 | 0.991935 |
| macro avg | 0.990196 | 0.993333 | 0.991658 | 124.000000 |
| weighted avg | 0.992173 | 0.991935 | 0.991955 | 124.000000 |
| Test Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Class_0 | 1.000000 | 1.000000 | 1.000000 | 18.000000 |
| Class_1 | 1.000000 | 0.952381 | 0.975610 | 21.000000 |
| Class_2 | 0.937500 | 1.000000 | 0.967742 | 15.000000 |
| accuracy | 0.981481 | 0.981481 | 0.981481 | 0.981481 |
| macro avg | 0.979167 | 0.984127 | 0.981117 | 54.000000 |
| weighted avg | 0.982639 | 0.981481 | 0.981554 | 54.000000 |
In this classification, the learning rate of your optimizer changes over time.
tf.keras.backend.clear_session()
classifier = tf.estimator.DNNClassifier(feature_columns=my_feature_columns,
# Hidden layers.
hidden_units= [1024, 512, 256],
# The model must choose between 3 classes.
n_classes=len(Labels_dict),
optimizer=lambda: tf.keras.optimizers.Adam(learning_rate=tf.compat.v1.train.exponential_decay(learning_rate=0.1,
global_step=tf.compat.v1.train.get_global_step(), decay_steps=IT,decay_rate=0.96)))
#
classifier.train(input_fn=lambda: input_fn(X_train, y_train, training=True), max_steps= IT)
result = classifier.evaluate(input_fn=lambda: input_fn(X_test, y_test, training=False))
clear_output()
display(pd.DataFrame(result, index = ['']).round(4))
| accuracy | average_loss | loss | global_step | |
|---|---|---|---|---|
| 0.9815 | 224.856201 | 224.856201 | 500 |
# converting y_test to categorical
y_test_cat = tf.keras.utils.to_categorical(y_test, num_classes = len(Labels_dict), dtype='float32')
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
ROC_Curve(y_test_cat, probs, n_classes = len(Labels_dict), FS = 8)
# Train
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_train, y_train, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Train = pd.DataFrame(metrics.classification_report(y_train, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Train = metrics.confusion_matrix(y_train, y_pred)
# Test
pred_dicts = list(classifier.predict(input_fn=lambda: input_fn(X_test, y_test, training=False)))
clear_output()
probs = np.array([pred['probabilities'] for pred in pred_dicts])
y_pred = np.argmax(probs, axis = 1).reshape(-1,1)
Reports_Test = pd.DataFrame(metrics.classification_report(y_test, y_pred, target_names=list(Labels_dict.values()),
output_dict=True)).T
CM_Test = metrics.confusion_matrix(y_test, y_pred)
Reports_Train = Reports_Train.reset_index().rename(columns ={'index': 'Train Set'})
Reports_Test = Reports_Test.reset_index().rename(columns ={'index': 'Test Set'})
display(Reports_Train.style.hide(axis='index').set_properties(**{'background-color': 'HoneyDew', 'color': 'Black'}).\
set_properties(subset=['Train Set'], **{'background-color': 'SeaGreen', 'color': 'White'}))
display(Reports_Test.style.hide(axis='index').set_properties(**{'background-color': 'Azure', 'color': 'Black'}).\
set_properties(subset=['Test Set'], **{'background-color': 'RoyalBlue', 'color': 'White'}))
PD = dict(FS = (10, 6), annot_kws = 14, shrink = .6, Labels = list(Labels_dict.values()))
Confusion_Mat(CM_Train, CM_Test, PD = PD, n_splits = None)
| Train Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Class_0 | 1.000000 | 1.000000 | 1.000000 | 41.000000 |
| Class_1 | 1.000000 | 1.000000 | 1.000000 | 50.000000 |
| Class_2 | 1.000000 | 1.000000 | 1.000000 | 33.000000 |
| accuracy | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| macro avg | 1.000000 | 1.000000 | 1.000000 | 124.000000 |
| weighted avg | 1.000000 | 1.000000 | 1.000000 | 124.000000 |
| Test Set | precision | recall | f1-score | support |
|---|---|---|---|---|
| Class_0 | 1.000000 | 1.000000 | 1.000000 | 18.000000 |
| Class_1 | 0.954545 | 1.000000 | 0.976744 | 21.000000 |
| Class_2 | 1.000000 | 0.933333 | 0.965517 | 15.000000 |
| accuracy | 0.981481 | 0.981481 | 0.981481 | 0.981481 |
| macro avg | 0.984848 | 0.977778 | 0.980754 | 54.000000 |
| weighted avg | 0.982323 | 0.981481 | 0.981378 | 54.000000 |